Marketing Attribution Platforms: Is It Smarter to Buy or Build In-House?

The decision between outsourcing or building an in-house marketing attribution system isn't as clear-cut as you think. Consider these three factors before taking the leap.

Companies all over the globe are constantly collecting data that has the potential to provide some valuable insights into consumers — that is, of course, if they can quantify the results.

This is why marketers turn to marketing attribution, where value can be assigned to the various touchpoints that led to a purchase. Whatever role a certain media exposure played in driving a customer through the sales funnel gets some sort of credit, allowing marketers to better plan future marketing spend.

Some companies initially tapped outside vendors and consultants to handle this process — vendors that had already built the software necessary for attribution models. However, because of frustrations with the early deployments of first-generation attribution providers, some of these marketers brought this process in-house.

Marketers who desire to own their company’s analytics internally do not want to wait around to act on data-driven decisions. In their minds, building their own attribution capabilities lends to greater agility and ensures the risk of exposing high-value data to the public remains low, which can quell any nerves about a potential security breach.

There’s also a (false) perception that if they build in-house, costs would go down and be cheaper in the long run. With a growing number of data scientists in the market, it just reinforces the perception that a martech vendor or consulting agency is no longer necessary.

However, many companies that have taken attribution in-house are turning back to analytics providers after struggling to run the process themselves. It can be difficult to build and manage a platform, even with competency in data, analytics, and measurement.

The decision to build or to buy isn’t as clear-cut as you might think — consider the following before making your final decision:

1. Determine whether you have the right expertise for the task.

Someone in your organization will need to oversee the attribution process, especially when building it in-house. Consider whether you have the manpower to manage the program. Do you have the resources and the right people for getting the program off the ground? You’ll need data science experts with experience building predictive models and an understanding of the nuances of machine learning approaches and the advantages and disadvantages of each. You’ll need data architects who have experience with extraction-transformation-loading (ETL) methodologies and approaches, tech solutions architects to understand what tools need to be integrated and how, data visualizers to translate data science language into marketing language so marketers can act on the insights, and perhaps a project manager to keep everything on track.

If you don’t already have the right people in your organization to run an in-house program, you’ll need to decide whether it’s more beneficial to hire those people or buy a platform from a third-party vendor. If it’s important to you that your platform is in-house, you’ll need to recruit, hire, train, and retain all these people. If you can’t afford that (financially or time-wise), it might be wiser to buy rather than build.

2. Consider how much you can reasonably afford.

Software can be expensive, and if you build in-house, you’ll need to consider the costs of building software for data collection/ETL, cloud hosting or physical hardware, integrated machine learning and artificial intelligence frameworks, data operations, quality assurance for deployment, and ongoing care for the platform. All of this will also require significant manpower — you might need at least 15 full-time employees to develop and manage the platform — and their salaries alone can cost hundreds of thousands of dollars per year.

It’s also not just employee and infrastructure costs that you’ll need to consider when building an in-house marketing attribution practice. There’s also the cost of getting all the external pieces integrated — for example, you’ll need to connect with ad servers, data management platforms, customer data platforms, and various other data sources, each unique in its own right. These costs can add up quickly, and if you’re set on building in-house, you’ll need to be prepared to make a significant investment.

3. Think about how much time you have.

If you build in-house, it can take years before the platform is up and running and ready to go. The data experts need time to put together all the pieces and build a repeatable process that marketers can benefit from — and it’s a long process. Even lightweight models that don’t require as much effort to build can still take several months, so if you’re planning to go the in-house route, make sure you understand the timeline before taking the leap. If your company is in a rapidly changing competitive landscape, think about whether you can wait a year or two to start taking advantage of these marketing insights.

Marketers have more data than ever at their fingertips, which can be a double-edged sword. While you have access to a wealth of information about your customers and how they interact with your brand, it can be challenging to organize all that data to glean valuable insights. Marketing attribution is critical if you want to make the most of your media buys. For any attribution model to be successful (in-house or provider), you need a team in place to analyze the results and make decisions using these results that improve business performance.

But the first step is deciding whether to buy a platform from a vendor or build the capability yourself in-house. Making the right decision will not only improve your marketing efforts, but it can also save you money in the long run and increase your company’s bottom line.

Brian Baumgart is a serial entrepreneur and angel investor with over 19 years of experience in advertising technology. He is currently the co-founder and CEO of Conversion Logic, a platform that provides marketers with the most accurate and advanced cross-channel analytics powered by award-winning machine learning and AI capabilities.

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